Monitoring the dynamics of the productivity of ocean water and how it affects fisheries is essential for management. It requires data on proper spatial/temporal scales, which can be provided by operational ocean colour satellites. However, accurate productivity data from ocean colour imagery is only possible with proper validation of, for instance, the atmospheric correction applied to the images. In situ water reflectance data is of great value due to the requirements for validation and it is traditionally measured with the Surface Acquisition System (SAS) solar tracker system. Recently, an application, `HydroColor', was developed for mobile devices to acquire water reflectance data. We examine the accuracy of the water reflectance acquired by HydroColor with the help of trained and untrained citizens under different environmental conditions. We used water reflectance data acquired by SAS solar tracker and HydroColor onboard the BC ferry Queen of Oak Bay from July to September 2016. Monte Carlo permutation F-tests were used to assess whether the differences between measurements collected by SAS solar tracker and HydroColor with citizens were significant. Results showed that citizen HydroColor measurements were accurate in red, green, and blue bands, as well as red/green and red/blue ratios under different environmental conditions. Piecewise models were developed for correcting HydroColor blue/green water reflectance ratios based on the SAS solar tracker measurements. In addition, we found that a trained citizen obtained higher quality HydroColor data under clear skies at noon.

Title: Host Contact Structure is Important for the Recurrence of Infulenza A
Speaker: Juan Jaramillo, University of Victoria
Date and time:
06 Dec 2017,
10:00am -
11:00am
Location: Clearihue Building Room B007Read full description

Notice of the Final Oral Examination for the Degree of Master of Science

of

JUAN M. JARAMILLO

BSc (University of Victoria, 2015)

“Host Contact Structure is Important for the Recurrence of Infulenza A”

An important characteristic of influenza A is its ability to escape host immunity through antigenic drift. A novel influenza A strain that causes a pandemic confers full immunity to infected individuals, yet because of antigenic drift, these individuals have decreased immunity to drifted strains. We compute the required decrease in immunity so that a recurrence is possible. Models for influenza A must make assumptions on the contact structure on which the disease spreads. By computing the reproduction number, we show that the classical random mixing assumption predicts an unrealistically large decrease of immunity before a recurrence is possible. We improve over the classical random mixing assumption by incorporating a contact network structure. A complication of contact networks is correlations induced by the initial pandemic. Thus, we provide a novel analytic derivation of such correlations and show that contact networks may require a dramatically smaller drop in immunity before recurrence. Hence, the key new insight is that on contact networks the establishment of a new strain is possible for much higher immunity levels of previously infected individuals than predicted by the commonly used random mixing assumption. This suggests that stable contacts like classmates, coworkers and family members are a crucial path for the spread of influenza in human population.

Dr. Viggo Andreasen, Department of Science and Environment, Roskilde University

Chair of Oral Examination:

Dr. Dzifa Dordunoo, School of Nursing, UVic

Abstract

A pandemic subtype of influenza A sometimes replaces (e.g., in 1918, 1957, 1968) but sometimes coexists (e.g., in 1977) with the previous seasonal subtype. This research aims to determine a condition for replacement or coexistence of influenza subtypes. We formulate a hybrid model for the dynamics of influenza A epidemics taking into account cross-immunity of influenza strains depending on the most recent seasonal infection. A combination of theoretical and numerical analyses shows that for very strong cross-immunity between seasonal and pandemic subtypes, the pandemic cannot invade, whereas for strong and weak cross-immunity there is coexistence, and for intermediate levels of cross-immunity the pandemic may replace the seasonal subtype.

Cross-immunity between seasonal strains is also a key factor of our model because it has a major influence on the final size of seasonal epidemics, and on the distribution of susceptibility in the population. To determine this cross-immunity, we design a novel statistical method, which uses a theoretical model and clinical data on attack rates and vaccine efficacy among school children for two seasons after the 1968 A/H3N2 pandemic. This model incorporates the distribution of susceptibility and the dependence of cross-immunity on the antigenic distance of drifted strains. We find that the cross-immunity between an influenza strain and the mutant that causes the next epidemic is 88%. Our method also gives an estimated value 2.15 for the basic reproduction number R0 of the 1968 pandemic influenza.

Our hybrid model agrees qualitatively with the observed subtype replacement or coexistence in 1957, 1968 and 1977. However, our model with the homogeneous mixing assumption significantly over estimates the pandemic attack rate. Thus, we modify the model to incorporate heterogeneity in the contact rate of individuals. Using the determined values of cross-immunity and R0, this modification lowers the pandemic attack rate slightly, but it is still higher than the observed attack rates.

Sea ice plays a key role in the global climate system. Indeed, through the albedo effect it reflects significant solar radiation away from the oceans, while it also plays a key role in the momentum and heat transfer between the atmosphere and ocean by acting as an insulating layer between the two. Furthermore, as more sea ice melts due to climate change, additional fresh water is released into the upper oceans, affecting the global circulation of the ocean as a whole. While there has been significant effort in recent decades, the ability to simulate sea ice has lagged behind other components of the climate system and most Earth System Models fail to capture the observed losses of Arctic sea ice, which is largely attributed to our inability to resolve sea ice dynamics. The most widely accepted model for sea ice dynamics is the Viscous-Plastic (VP) rheology, which leads to a very non-linear set of partial differential equations that are known to be intrinsically difficult to solve numerically. This work builds on recent advances in solving these equations with a Jacobian-Free Newton-Krylov (JFNK) solver. We present an improved JFNK solver, where a fully second order discretization is achieved via the Crank Nicolson scheme and consistency is improved via a novel approach to the rheology term. More importantly, we present a significant improvement to the Jacobian approximation used in the Newton iterations, and partially form the action of the matrix by expressing the linear and nearly linear terms in closed form and approximating the remaining highly non-linear term with a second order approximation of its Gateaux derivative. This is in contrast with the previous approach which used a first order approximation for the Gateaux derivative of the whole functional. Numerical tests on synthetic equations confirm the theoretical convergence rate and demonstrate the drastic improvements seen by using a second order approximation in the Gateaux derivative. To produce a fast and efficient solver for VP sea ice dynamics, the improved JFNK solver is then coupled with a nonoscillatory, central differencing scheme for transporting sea ice as well as a novel method for tracking the ice domain using a level set method. Two idealized test cases are then presented and simulation results discussed, demonstrating the solver's ability to efficiently produce Viscous- Plastic, physically motivated solutions.

SUMS Pi day celebration and triathlon

Happy Pi Day!
Celebrate with #UVicScience and Food Services at a pie pop-up shop on Wednesday, where they'll be giving away 314 pieces of pie at 3:14pm by the #UVicScience Petch fountain!
And SUMS (Students in Undergraduate Mathematics and Statistics) will be hosting a math competition and party in HSD A264 starting at 2pm. See http://www.math.uvic.ca/~sums/

March 14 is Pi Day!
Celebrate with #UVicScience and Food Services at a pie pop-up shop on Wednesday, where they'll be giving away 314 pieces of pie at 3:14pm by the #UVicScience Petch fountain!
And SUMS (Students in Undergraduate Mathematics and Statistics) will be hosting a math competition and party in HSD A264 starting at 2pm. See http://www.math.uvic.ca/~sums/